Distilbert-base-uncased-finetuned-ner Open Source Model - Free and Efficient Named Entity Recognition

Distilbert Base Uncased Finetuned Ner

Developed by Udi-Aharon

This model is a lightweight version based on DistilBERT, fine-tuned on the conll2003 dataset for named entity recognition tasks.

Sequence Labeling

Transformers

Open Source License:Apache-2.0 #Named Entity Recognition #High-precision NER #DistilBERT Optimization

Downloads 15

Release Time : 5/10/2022

Model Overview

This is a fine-tuned DistilBERT model specifically designed for Named Entity Recognition (NER) tasks, demonstrating excellent performance on the conll2003 dataset.

Model Features

Efficient and Lightweight

Based on the DistilBERT architecture, it is more lightweight and efficient than standard BERT models while maintaining good performance.

High-precision NER

Achieves 92.4% precision and 93.5% recall on the conll2003 dataset.

Fast Inference

Due to the DistilBERT architecture, inference speed is faster than the full BERT model.

Model Capabilities

Named Entity Recognition

Text Token Classification

Entity Extraction

Use Cases

Information Extraction

News Entity Extraction

Identify entities such as person names, locations, and organization names from news texts.

F1 score reaches 0.9295

Document Analysis

Process key entity recognition in legal or medical documents.

Data Preprocessing

Knowledge Graph Construction

Extract entity information for knowledge graph construction.

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2396	1.0	878	0.0715	0.9135	0.9228	0.9181	0.9805
0.051	2.0	1756	0.0617	0.9192	0.9334	0.9263	0.9826
0.0295	3.0	2634	0.0615	0.9243	0.9347	0.9295	0.9837

Property	Details
Model Type	distilbert-base-uncased-finetuned-ner
Training Data	conll2003
Metrics	Precision, Recall, F1, Accuracy

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Distilbert Base Uncased Finetuned Ner

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